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Cited by 31Speech and dialogue systemsUsability and User Interface DesignNatural Language Processing Techniques

ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles

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Petridis, Savvas, Wedin, Benjamin D, Wexler, James, Pushkarna, Mahima, Donsbach, Aaron, Goyal, Nitesh, Cai, Carrie J, Terry, Michael·2024·Proceedings of the 29th International Conference on Intelligent User Interfaces
DOI 10.1145/3640543.3645144
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Abstract

Large language model (LLM) prompting is a promising new approach for users to create and customize their own chatbots. However, current methods for steering a chatbot’s outputs, such as prompt engineering and fine-tuning, do not support users in converting their natural feedback on the model’s outputs to changes in the prompt or model. In this work, we explore how to enable users to interactively refine model outputs through their feedback, by helping them convert their feedback into a set of principles (i.e. a constitution) that dictate the model’s behavior. From a formative study, we (1) found that users needed support converting their feedback into principles for the chatbot and (2) classified the different principle types desired by users. Inspired by these findings, we developed ConstitutionMaker, an interactive tool for converting user feedback into principles, to steer LLM-based chatbots. With ConstitutionMaker, users can provide either positive or negative feedback in natural language, select auto-generated feedback, or rewrite the chatbot’s response; each mode of feedback automatically generates a principle that is inserted into the chatbot’s prompt. In a user study with 14 participants, we compare ConstitutionMaker to an ablated version, where users write their own principles. With ConstitutionMaker, participants felt that their principles could better guide the chatbot, that they could more easily convert their feedback into principles, and that they could write principles more efficiently, with less mental demand. ConstitutionMaker helped users identify ways to improve the chatbot, formulate their intuitive responses to the model into feedback, and convert this feedback into specific and clear principles. Together, these findings inform future tools that support the interactive critiquing of LLM outputs.

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  • Some frameworks already experiment with using feedback from assertions or user grades to refine prompts.

    “Some frameworks already experiment with using feedback from assertions or user grades to refine prompts [35, 39, 48].”

    From Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences (2024) · cites this paper as [39]

    Section 2.2 says ConstitutionMaker converts users’ feedback on prompt outputs into principles incorporated back into the prompt. Section 5.2 describes kudos, critiques, and rewrites as feedback, but does not describe feedback from assertions or user grades. The prompt-refinement point is supported; the specified feedback forms are not. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • This suggests an optimization loop akin to ConstitutionMaker and DSPy, but for assertion generation and validation rather than prompt optimization.

    “This suggests an optimization loop akin to ConstitutionMaker and DSPy [26, 39], but for assertion generation and validation , rather than prompt optimization.”

    From Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences (2024) · cites this paper as [39]

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Cited by

Sorted by citations

Authors
Petridis, S., Wedin, B. D., Wexler, J., Pushkarna, M., Donsbach, A., Goyal, N., Cai, C. J., & Terry, M.
Affiliations
  • Google (United States) (US)
  • Google Research, United States
ORCID
Venue
Proceedings of the 29th International Conference on Intelligent User Interfaces
Publisher
ACM
Published
2024-03-18
Pages
853-868
DOI
10.1145/3640543.3645144
OpenAlex
W4393970715
Cited by
31
References
10
Citations by year
2026: 10 · 2025: 15 · 2024: 6
FWCI
8.95
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
large language models, LLM prompting, user feedback, principles, prompt engineering

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